System and method for using artificial intelligence to enable elevated temperature detection of persons using commodity-based thermal cameras

ABSTRACT

A multi-sensor threat detection system and method for elevated temperature detection using commodity-based thermal cameras and mask wearing compliance using optical cameras. The proposed method does not rely on the accuracy of thermal cameras, but the combination of mathematics, statistics, machine learning, artificial intelligence, computer vision and Manifold learning to construct a classifier, or set of classifiers, that are able to, either alone or working as an ensemble, evaluate a person as being ‘normal temperature’ or ‘elevated temperature’ by virtue of ‘how they present to the camera’ vs. any absolute temperature measurements from the camera itself.

CROSS REFERENCE TO RELATED APPLICATIONS

The application claims priority to and the benefit of U.S. ProvisionalPatent Application Ser. No. 63/029,609, entitled “SYSTEM AND METHOD FORUSING ARTIFICIAL INTELLIGENCE TO ENABLE ELEVATED TEMPERATURE DETECTIONOF PERSONS USING COMMODITY-BASED THERMAL CAMERAS”, filed on May 25,2020, the disclosure of which is incorporated herein by reference in itsentirety.

BACKGROUND

The embodiments described herein relate to security and surveillance, inparticular, technologies related to video recognition threat detection.

Existing elevated temperature systems simply utilize the temperaturemeasurements as provided by the thermal camera itself. Some systems useartificial intelligence (AI) to detect the face or other regions ofinterest (e.g., arms and legs) to enable capturing the temperaturemeasurements for specific regions while ignoring other benign objectslike, for example, a hot beverage. Some systems even focus in onspecific areas of the facial region like the inner canthus or tearducts, which have been shown to most accurately reflect core-bodytemperature. While novel, all such strategies fail when using acommodity-based camera that does not have a tight enough temperaturevariance to be suitable for evaluating persons for elevatedtemperatures. For example, the popular Axis Q2901-E Temperature AlarmCamera has a temperature variance of +/−5° C. (+/−9° F.) accuracyrendering it inappropriate for such use cases.

Academic research has shown that early detection of contagious pathogenssuch as H1N1, Seasonal Influenza (Flu), or Coronavirus outbreaks such asSARS-CoV, MERS-CoV or SARS-CoV-2 (CoVID-19) could help slow rates ofinfection, limit the impact on regional community health-care servicesand increase the probability for timely treatment.

A platform for threat detection solutions is envisioned. This softwareplatform may use thermal cameras and other sensor technologies fortemperature measurements for threat detection, including such dangers asweapons and physical threats, as well as early detection of viralpathogens such as COVID-19, H1N1 Influenza and other microbiologicalthreats to prevent spread of these pathogens.

SUMMARY

A multi-sensor threat detection system and method for elevatedtemperature detection using commodity-based thermal cameras and maskwearing compliance using optical cameras. The proposed method does notrely on the accuracy of thermal cameras, but the combination ofmathematics, statistics, machine learning and computer vision toconstruct a classifier or set of classifiers that are able to, eitheralone or working as an ensemble, evaluate a person as being ‘normaltemperature’ or ‘elevated temperature’ by virtue of ‘how they present tothe camera’ vs. any absolute temperature measurements from the cameraitself.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an exemplary thermal module.

FIG. 2 is a diagram illustrating checkpoint screening.

FIG. 3 is a diagram illustrating thermal detection of elevated bodytemperature.

FIG. 4 is a diagram illustrating a facial mask compliance detectionmodule.

FIG. 5 is a diagram illustrating effective distance for facial maskdetection for health & safety.

FIG. 6 is a diagram illustrating equipment & specs required for Module 1and 2 including both thermal and mask components.

FIG. 7 is a diagram illustrating installation considerations for Module1 and 2 including both thermal and mask components.

FIG. 8 is a diagram illustrating a deployment for a building interiorscenario.

FIG. 9 is a diagram illustrating a deployment for a building exteriorscenario including both thermal and mask components.

FIG. 10 is a diagram illustrating a process for secondary screeningincluding both thermal and mask components.

FIG. 11 is a diagram illustrating thermal camera color intensity.

FIG. 12 is a diagram illustrating a sample of elevated (left) and normaltemperature (right).

FIG. 13 is a diagram illustrating head regions.

FIG. 14 is a diagram illustrating regions of interest for classifying aselevated (left) or normal temperature (right).

FIG. 15 is a diagram illustrating evaluating noise of resulting colourchannels with elevated temperature shown on the left and normaltemperature shown on the right.

FIGS. 16A and 16B are diagrams illustrating outputs to the end user.

FIG. 17 is a diagram illustrating an example of training and using aManifold learning model.

FIG. 18 is a diagram illustrating a classification example illustratingonline “normal and “elevated body temperature”.

FIG. 19 is a system diagram of an exemplary threat detection system.

DETAILED DESCRIPTION

In a preferred embodiment, a multi-sensor covert threat detection systemis disclosed. This covert threat detection system utilizes software,artificial intelligence and integrated layers of diverse sensortechnologies (i.e., cameras, etc.) to deter, detect and defend againstactive threats (i.e., detection of guns, knives or fights) or healthrelated risks (i.e., presence of a fever or noncompliance of recommendedmask wearing or social distancing) before these threat events occur.

The threat detection system enables the system operator to easilydetermine if the system is operational without requiring testing withactual triggering events. This system also provides more situationalinformation to the operator in real time as the incident is developing,and showing them what they need to know, when they need to know it.

FIG. 19 is a system diagram of an exemplary threat detection system. Asseen in FIG. 19, threat detection system 100 consist of one or morecameras 102 configured to record video data (images and audio). Cameras102 is connected to sensor or sensor acquisition module 104. Once thedata is acquired, the data is sent simultaneously to an AI AnalyticsEngine 106 and Incident Recorder Database 114. AI Analytics Engine 106analyzes the data with input from an Incident Rules Engine 108.Thereafter, the data is sent to an application program interface (API)110 or sent to 3^(rd) party services 118. The output form the API 110will be sent to a user interface (UI) 112 or graphical user interface(GUI). Furthermore, the output from the API 110 and AI Analytics Engine106 will be further recorded at the Incident Recorder Database 114.

While there do exist high-end thermal cameras that are suitable forevaluating elevated temperatures in people, most commodity-based camerashave too wide a temperature variance to be used for this purpose. Havingan analysis technique capable of accurately classifying persons as‘normal temperature’ or ‘elevated temperature’ using any combination ofthermal video, or thermal video frames, from commodity-based cameraswithout regard to what the actual absolute temperature measurement is,would be a significant step towards making such capabilities accessibleto a wider consumer base and thus increasing our ability to protectagainst contagions by highlighting the subset of people that presentwith a higher than normal temperature. This is not meant as a silverbullet but would allow locations to highlight those symptomatic peoplethat should be sent for secondary screening. It should also be notedthat skin temperature may not always reflect core-body temperature.

The approaches used in this disclosure do not rely on the accuracy ofthermal cameras. Instead, we use any combination of mathematics,statistics, machine learning, computer vision to construct a classifieror set of classifiers, that are able to, either alone or working as anensemble, evaluate a person as being ‘normal temperature’ or ‘elevatedtemperature’ by virtue of ‘how they present to the camera’ vs. anyabsolute temperature measurements from the camera itself. Such learningapproaches, as Manifold Learning, take the less obvious ‘higher-level’features and map them to a lower dimensional representation where thesenew ‘lower-level’ features may be analyzed to get that classification of‘normal’ or ‘elevated body temperature.

With an intuition of what these lower-level features are due to thereduced mapping of the Manifold Learning routine, one embodimentincorporates a sufficiently trained Manifold Learning backbone thatidentifies relative geometric and intensity features that normalizeacross the thermal video data down to the single video-frame level,without regard to absolute temperature measurements from the camera,which may not be accurate enough on its own. Identifying such featuresremoves the problem where the temperature spectrum of a thermal camerais not consistent across time because it is based on the measured rangeof absolute temperatures present within the video frame in any singleinstance. Relative features like this could be used to evaluate forelevated temperatures regardless of the absolute temperaturemeasurements present within a frame, which can vary by camera, byenvironment and even by time of day.

Artificial Intelligence (AI) has been shown to be influenced by tinydetails that are imperceptible to humans. Ilyas et al. provide evidencethat AI can pick up on characteristics that are invisible to humans butare present in the real world. The approach described here utilizes thisphenomenon along with the geometric information, among othermathematical, statistical, machine learning, and computer visiontechniques to classify persons as being ‘normal temperature’ or‘elevated temperature’ without regard to the actual absolute temperaturemeasurements of the thermal camera. FIG. 11 is an example of an AIface-detection system to be used.

Health & Safety Products:

A multi-sensor threat detection system such as the Patriot OneTechnologies PATSCAN Video Recognition System (VRS) may include productsand modules for different verticals and markets. As an example, thePATSCAN VRS platform has a Health & Safety Product offering including anElevated Body Temperature & Identification module and a Health & SafetyCompliance Detection module. Furthermore, in other embodiments, theHealth and Safety module includes mask detection/compliance.

Module 1: VRT-T Elevated Body Temperature Screening & Identification

FIG. 1 is a diagram illustrating an exemplary thermal module. Thismodule may utilize artificial intelligence (AI) in further versions ofthe software module to identify and analyze instances of elevated bodytemperature. Anomalous detection outside a nominal temperature rangeresults in an alert transmitted to onsite security for further actionemploying standard operating procedures as defined by the facilitymanagement or organization. Providing early indications of diseasetransmissibility of individuals within populous or crowded locations canprovide significant improvements in overall safety.

The thermal module of FIG. 1 is suitable for use in offices, carefacilities and other locations where organizations wish to screenincoming employees and visitors for possible elevated body temperaturesindicative of fever from flu-like symptoms. The solution can be used toquickly screen individuals or a queue of people entering a facility in acontrolled checkpoint environment. FIG. 2 is a diagram illustratingcheckpoint screening leveraging these advanced AI techniques.

FIG. 3 is a diagram illustrating thermal detection of elevated bodytemperature. This module is designed for deployment on a single thermalcamera at entrances for the detection of elevated body temperatures,which may indicate a fever related to a virus or contagion. It issuitable for an operator stationed inside or outdoors, or as a temporarystation for a dedicated screening room.

As seen in FIG. 3, this module can detect elevated temperature from arange of 6 feet to 15 feet. This module can be collapsible for easytransport, is study and provides for ergonomic setup for laptop, camerasand power supply.

Module 2: VRS-HS Health & Safety Face Mask Compliance Detection

FIG. 4 is a diagram illustrating a facial mask compliance detectionmodule. According to FIG. 4, the face mask compliance detection moduleuses computer vision analytics that leverages common off-the-shelfdigital video camera technology. The module is designed for deploymenton single or multiple digital video camera networks for the detection offace mask compliance to assist in preventing the spread of viralpathogens. This module is ideal for deployments for hospitals, businessoffices, event/sports venues, government buildings, retail, schools anduniversities and other locations where public gathers, and healthconcerns are critical.

An alert/no-alert notification can be programmed to the user or system:

-   -   No Alert Notification: MASK IN-PLACE/COMPLIANT    -   Alert Notification: NO MASK/NON-COMPLIANT

FIG. 5 is a diagram effective distance for facial mask detection forhealth & safety. As seen in FIG. 5, this module can be effective for upto 50 feet.

Equipment & Specs:

FIG. 6 is a diagram illustrating equipment & specs required for Module 1and 2 including both thermal and mask components. According to FIG. 6,equipment and specs include an approved thermal camera, an approveddigital video camera, an approved laptop computer and threat detectionsystem software modules for thermal video recognition (i.e., thermalelevated body temperature module) and health and safety (i.e., health &safety face mask detection module).

Installation Considerations:

FIG. 7 is a diagram illustrating installation consideration for Module 1and 2 including both thermal and mask components. According to FIG. 7,locations are reviewed for effectiveness based on environmentalvariables. These variables include distance from a camera to detectionzone, number of persona present, screen area height, width and length,camera angle, fluctuating skin temperatures and weather conditions. Thevariables are all factored into the artificial intelligence (AI)software for each unique deployment.

Health & Safety Threat Installed Deployment Scenario:

FIG. 8 is a diagram illustrating a deployment for a building interiorscenario. As seen in FIG. 8, a building entry scenario is envisioned.According to FIG. 8, one or more face mask detection cameras (A) can bedeployed at the entrance. A body temperature camera (B) is placedopposite the entrance. Security guards (C) with mobile application maybe situated nearby. Further, once an event is observed, an alert is sentto an onsite security guard (D).

FIG. 9 is a diagram illustrating a deployment for a building exteriorscenario including both thermal and mask components. A fully, integratedcamera installation is envisioned. This is suitable for operationstationed inside or outside. The system Observes and alerts withindedicated screening entry area, and sends mobile alert to onsitesecurity.

FIG. 10 is a diagram illustrating a process for secondary screeningincluding both thermal and mask components. A workflow for secondaryscreen is as follows:

-   -   Person enters screening area    -   Person remains in screening area and monitor for following        triggers:        -   Pass: Nominal temperature, mask in place, proceed        -   Warn: Missing mask, moderate temperature, elective advisory        -   Screen: Elevated temperature, alert, mask compliance,            screening recommended    -   Thermal Recognition    -   Secondary Inspection    -   Pass

FIG. 11 is a diagram illustrating thermal camera color intensity. Thisexample of lower-level features is just for illustration purposes but,in fact, artificial intelligence (AI) has been shown to be influenced bytiny details that are imperceptible to humans. A recent paper by “Ilyaset al.” provides evidence that AI can pick up on characteristics thatare invisible to humans but are present in the real world. The approachdescribed here utilizes this phenomenon, along with the geometricinformation among other mathematical, statistical, machine learning,computer vision and learning techniques to classify persons as being‘normal temperature’ or ‘elevated temperature’ without regard to theactual absolute temperature measurements of the thermal camera.

Referring to FIG. 11, one objective is to protect any analysis ofthermal video or thermal video frames that can be used to classify aperson as being ‘normal temperature’ or ‘elevated temperature’ withoutregard to the absolute temperature measurements of the thermal camera.

As described above, the analysis used to distinguish between ‘normal’and ‘elevated’ temperature can range from mathematical models to themore modern deep learning models, or any combination thereof. An exampleof each follows.

Mathematical Model:

This capability can be incorporated into organizational workflows insupport of emerging pandemic management procedures. Available as eithersingle or multi-person screening, this approach can be deployed anywherewithin an organization including but not limited to entryways, lobbies,or other vulnerable areas where it would be valuable to screen forpersons with elevated temperatures.

FIG. 12 is a diagram illustrating a sample of elevated (left) and normaltemperature (right). Given an input thermal video or thermal frame (FIG.12), the image is passed through an algorithm which first detects thepresence and location of a person's exposed skin (e.g., head/face, arms,legs).

FIG. 13 is a diagram illustrating head/face regions. FIG. 14 is adiagram illustrating regions of interest for classifying as elevated(left) or normal temperature (right). The sub-image containing onlythese regions (FIG. 13) is passed through any number of preprocessingstages like, for example, pixel filtering to remove the irrelevantpixels (FIG. 14). Irrelevant pixels are defined as those outside of theexposed skin regions, or those that do not exhibit enough colourcontrast to be correlated with elevated temperatures (e.g., contrastingbrightness or intensity of pixels across the regions of interest).

FIG. 15 is a diagram illustrating evaluating noise of resulting colourchannels with elevated temperature shown on the left and normaltemperature shown on the right. The remaining sub-image is then analyzedfurther by breaking it into colour channels and programmaticallyevaluating the noise of the resulting histograms, as shown in FIG. 15.According to FIG. 15, one can see that there appears to be (at leastaccording to the scale of the thermal temperatures in this image) adifference between the elevated subject (left) and the normal subject(right). This mathematical model can be automated and deployed assoftware to screen persons for elevated temperatures in real-timehighlighting those that require secondary screening.

An example of possible output to the end user are shown in FIGS. 16A and16B. According to FIG. 16B, a user interface displays the output of themask compliance module (left) and thermal screening module (right). Bothof these modules can be run simultaneously and the information can befused into one output for easier interpretation by security personnel.

Manifold Learning Model:

FIG. 17 is a diagram illustrating an example of training and using aManifold learning model. FIG. 18 is a diagram illustrating aclassification example illustrating online “normal and “elevated bodytemperature”. As seen in FIG. 17 and FIG. 18, more sophisticated modelcan be achieved using Manifold Learning approaches where a large datasetconsisting of images of persons exhibiting ‘normal temperatures’ and‘elevated temperatures’ are mapped to a lower dimensionalrepresentation. This creates a ‘learned’ network that can be used toclassify newly encountered examples for elevated temperatures based onlocal geometries and pixel intensities. Building a Manifold Learningmodel can be simplified due to its inherent clustering effect based onjust a few feature descriptors. FIG. 17 shows the training process. FIG.18 shows the online ‘normal’ and ‘elevated body temperature’classification example.

As discussed above, Manifold Learning can often find low level featuresthrough geometric intuition in images that are imperceptible to humans.It is this characteristic of Manifold Learning models, that they are notdependent on manual feature selection, that makes them particularlysuitable for evaluating and highlighting persons with elevatedtemperatures using only commodity-based lower precision thermal cameras.

In a further embodiment, disclosed herein is a multi-sensor threatdetection system used for elevated temperature detection, the systemcomprising a processor, a sensor acquisition module, an optical andthermal camera configured to capture image data using the sensoracquisition module, a plurality of connected algorithms that correlatespixel value to thermal reading using a Manifold learning algorithm and auser interface configured to provide notification and alerts,

The optical camera of the multi-sensor threat detection system furthercomprises a face mask detection camera. The thermal camera of themulti-sensor threat detection system further comprises a bodytemperature detection camera. The multi-sensor threat detection systemfurther comprising a mobile alert module configured to send alerts tomobile devices.

The multi-sensor threat detection system further comprisingadministering pre-screening guidelines to acquire a face image. Thepre-screening guidelines are selected from a list consisting of removingglasses, removing a mask, removing a hood, removing a hat, pulling backhair from the face, removing a scarf from the face.

The multi-sensor threat detection system further comprises the step ofre-cropping a face image and sending a cropped image from the noseupwards to a Manifold classifier of the Manifold learning algorithm. Thecropped image includes a subset of the face indicating key points aroundthe eyes.

The Manifold learning algorithm further comprises reducing thedimensionality down to 10-12 feature descriptors from the image andproviding the highest correlation to the ground truth number andunsupervised learning.

In a further embodiment, a computer-implemented method for elevatedtemperature detection using commodity-based thermal camera is disclosed.The method comprises receiving a series of input from frames from thethermal camera, using a deep learning algorithm to localize area of facefrom the frame images, cropping a face sub-frame from the image usingthe localized area of the face, sending the face sub-frame to a Manifoldlearning algorithm classifier to produce a classification for eachimage, conducting post-processing and determine whether there isevidence to support an anomalous reading to suggest a fever, if there isno fever detected, provide a pass notification indicating that thesubject can enter the facility, if there is an anomalous reading orevidence to support the anomalous reading, provide a fail notificationindicating that the subject is required to go to secondary screening ordenied entry and recording the fail response and data onto a computerserver for triaging.

The computer-implemented method for elevated temperature detectionwherein the steps to determine fever further comprising detectingevidence of a higher temperature, analyzing a plurality of framed imagesduring a temporal window based on pre-conditions and if a certainthreshold of framed images are being classified as a higher temperature,provide an output of fail notification.

According to the aforementioned computer-implemented method, a failnotification further comprises detection of higher temperature oranomalous temperature reading. The threshold further comprises 3 or moreframed images being classified as higher temperature.

In a further embodiment, a computer-implemented method for face maskdetection using an optical camera is disclosed, wherein the methodcomprising receiving a series of input from frames from the opticalcamera, using an object detection localization algorithm to localizearea of face from the frame images, creating a mask compliance model,sending the face frame to a mask compliance analytic for processing,conducting post-processing and determine whether a person is unmasked,if a person is present and the face is unmasked, provide a non-compliantcondition, generate an alert/notification using the user interface.

According to the above method, the object detection localizationalgorithm is selected from a list consisting of RetinaNet, Mask RCNN andYOLO. Furthermore, the step of creating a mask compliance model furthercomprises the step of training a plurality of different labelled imageson masking conditions. The masking conditions is selected from a listconsisting of people wearing masks, and people not wearing masks, in avariety of environments, camera angels and crowd densities.

According to the above method, the step of mask compliance analyticprocessing further comprising identifying in the image whether a personis in the image, and if so, determines the localization of their face ismasked or unmasked if enough of their face is present in the frame.

Implementations disclosed herein provide systems, methods and apparatusfor generating or augmenting training data sets for machine learningtraining. The functions described herein may be stored as one or moreinstructions on a processor-readable or computer-readable medium. Theterm “computer-readable medium” refers to any available medium that canbe accessed by a computer or processor. By way of example, and notlimitation, such a medium may comprise RAM, ROM, EEPROM, flash memory,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to storedesired program code in the form of instructions or data structures andthat can be accessed by a computer. It should be noted that acomputer-readable medium may be tangible and non-transitory. As usedherein, the term “code” may refer to software, instructions, code ordata that is/are executable by a computing device or processor. A“module” can be considered as a processor executing computer-readablecode.

A processor as described herein can be a general purpose processor, adigital signal processor (DSP), an application specific integratedcircuit (ASIC), a field programmable gate array (FPGA) or otherprogrammable logic device, discrete gate or transistor logic, discretehardware components, or any combination thereof designed to perform thefunctions described herein. A general purpose processor can be amicroprocessor, but in the alternative, the processor can be acontroller, or microcontroller, combinations of the same, or the like. Aprocessor can also be implemented as a combination of computing devices,e.g., a combination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration. Although described hereinprimarily with respect to digital technology, a processor may alsoinclude primarily analog components. For example, any of the signalprocessing algorithms described herein may be implemented in analogcircuitry. In some embodiments, a processor can be a graphics processingunit (GPU). The parallel processing capabilities of GPUs can reduce theamount of time for training and using neural networks (and other machinelearning models) compared to central processing units (CPUs). In someembodiments, a processor can be an ASIC including dedicated machinelearning circuitry custom-build for one or both of model training andmodel inference.

The disclosed or illustrated tasks can be distributed across multipleprocessors or computing devices of a computer system, includingcomputing devices that are geographically distributed. The methodsdisclosed herein comprise one or more steps or actions for achieving thedescribed method. The method steps and/or actions may be interchangedwith one another without departing from the scope of the claims. Inother words, unless a specific order of steps or actions is required forproper operation of the method that is being described, the order and/oruse of specific steps and/or actions may be modified without departingfrom the scope of the claims.

As used herein, the term “plurality” denotes two or more. For example, aplurality of components indicates two or more components. The term“determining” encompasses a wide variety of actions and, therefore,“determining” can include calculating, computing, processing, deriving,investigating, looking up (e.g., looking up in a table, a database oranother data structure), ascertaining and the like. Also, “determining”can include receiving (e.g., receiving information), accessing (e.g.,accessing data in a memory) and the like. Also, “determining” caninclude resolving, selecting, choosing, establishing and the like.

The phrase “based on” does not mean “based only on,” unless expresslyspecified otherwise. In other words, the phrase “based on” describesboth “based only on” and “based at least on.” While the foregoingwritten description of the system enables one of ordinary skill to makeand use what is considered presently to be the best mode thereof, thoseof ordinary skill will understand and appreciate the existence ofvariations, combinations, and equivalents of the specific embodiment,method, and examples herein. The system should therefore not be limitedby the above described embodiment, method, and examples, but by allembodiments and methods within the scope and spirit of the system. Thus,the present disclosure is not intended to be limited to theimplementations shown herein but is to be accorded the widest scopeconsistent with the principles and novel features disclosed herein.

What is claimed is:
 1. A multi-sensor threat detection system forelevated temperature detection, the system comprising: a processor; asensor acquisition module; an optical and thermal camera configured tocapture image data using the sensor acquisition module; a plurality ofconnected algorithms that correlate pixel value to thermal reading usinga Manifold learning algorithm; and a user interface configured toprovide notification and alerts.
 2. The system of claim 1 wherein theoptical camera further comprises a face mask detection camera.
 3. Thesystem of claim 1 wherein the thermal camera further comprises atemperature detection camera.
 4. The system of claim 1 furthercomprising a mobile alert module configured to send alerts to mobiledevices.
 5. The system of claim 1 further comprising administeringpre-screening guidelines to acquire a face image.
 6. The system of claim5 wherein pre-screening guidelines are selected from a list consistingof removing glasses, removing a mask, removing a hood, removing a hat,pulling back hair from the face, removing a scarf from the face.
 7. Thesystem of claim 1 further comprising the step of re-cropping a faceimage and sending a cropped image from the nose upwards to a Manifoldclassifier of the Manifold learning algorithm.
 8. The system of claim 7wherein the cropped image includes a subset of the face indicating keypoints around the eyes.
 9. The system of claim 1 wherein the Manifoldlearning algorithm further comprises reducing the dimensionality to10-12 feature descriptors from the image and providing the highestcorrelation to the ground truth number and unsupervised learning.
 10. Acomputer-implemented method for elevated temperature detection using acommodity-based thermal camera, the method comprising: receiving aseries of inputs from frames from the thermal camera; using a deeplearning algorithm to localize an area of a face from the frame images;cropping a face sub-frame from the image using the localized area of theface; sending the face sub-frame to a Manifold learning algorithmclassifier to produce a classification for each image; conductingpost-processing to determine if there is evidence to support ananomalous reading suggesting a fever; if there is no fever detected,provide a pass notification indicating that the subject can enter thefacility; if there is an anomalous reading or evidence to support theanomalous reading, provide a fail notification indicating that thesubject is required to go to secondary screening or denied entry; andrecording the fail response and associated data onto a computer server.11. The method of claim 10 wherein the steps to determine fever furthercomprise: analyzing a plurality of framed images during a temporalwindow based on pre-conditions; and if a certain threshold of framedimages are classified as a higher temperature, providing an output offail notification.
 12. The method of claim 11 wherein a failnotification further comprises detection of higher temperature oranomalous temperature reading.
 13. The method of claim 10 wherein thethreshold further comprises 3 or more framed images being classified ashigher temperature.
 14. A computer-implemented method for face maskdetection using an optical or thermal camera, the method comprising:receiving a series of input from frames from the camera; using an objectdetection localization algorithm to localize area of face from the frameimages; creating a mask compliance model; sending the face frame to amask compliance analytic for processing; conducting post-processing anddetermine whether a person is unmasked; if a person is present and theface is unmasked, provide a non-compliant condition; and generate analert or notification using a user interface.
 15. The method of claim 14wherein the object detection localization algorithm is selected from alist consisting of RetinaNet, Mask RCNN and YOLO.
 16. The method ofclaim 14 wherein the step of creating a mask compliance model furthercomprises the step of training with a plurality of different labelledimages on masking conditions.
 17. The method of claim 16 wherein imageson masking conditions are selected from a list comprising images ofpeople wearing masks, and people not wearing masks, people in a varietyof environments, a variety of camera angles and crowd densities.
 18. Themethod of claim 14 wherein the step of mask compliance analyticprocessing further comprises identifying from the image whether a personis in the image, and if so, determining from the localization of theirface if they are masked or unmasked if enough of their face is visiblein the frame.